{"id":"https://openalex.org/W7138227993","doi":"https://doi.org/10.1609/aaai.v40i5.37340","title":"On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition","display_name":"On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138227993","doi":"https://doi.org/10.1609/aaai.v40i5.37340"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i5.37340","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37340","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i5.37340","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070209606","display_name":"Adrian Cosma","orcid":"https://orcid.org/0000-0003-0307-2520"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adrian Cosma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129709951","display_name":"Andy Eduard Catruna","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andy Eduard Catruna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129669107","display_name":"Emilian Radoi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emilian Radoi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31104731,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"5","first_page":"3434","last_page":"3442"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.7419999837875366},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.7170000076293945},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5586000084877014},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.47269999980926514},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4431999921798706},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.40860000252723694},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3727000057697296},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.3580999970436096},{"id":"https://openalex.org/keywords/gait-analysis","display_name":"Gait analysis","score":0.3122999966144562}],"concepts":[{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.7419999837875366},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7170000076293945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6615999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5855000019073486},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5586000084877014},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.47269999980926514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.40860000252723694},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35830000042915344},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32359999418258667},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27869999408721924},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C70770792","wikidata":"https://www.wikidata.org/wiki/Q7239848","display_name":"Preferred walking speed","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.26669999957084656},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C91682802","wikidata":"https://www.wikidata.org/wiki/Q620538","display_name":"Multidimensional scaling","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2599000036716461}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i5.37340","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37340","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/37340","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/37340","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i5.37340","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37340","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1008782263","display_name":null,"funder_award_id":"351416","funder_id":"https://openalex.org/F4320311068","funder_display_name":"Health Resources in Action"}],"funders":[{"id":"https://openalex.org/F4320311068","display_name":"Health Resources in Action","ror":"https://ror.org/01ctnwa22"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gait":[0],"recognition":[1,37,85,100,193],"from":[2],"video":[3],"streams":[4],"is":[5],"a":[6,109,114],"challenging":[7],"problem":[8],"in":[9,26,53,122,149],"computer":[10],"vision":[11],"biometrics":[12],"due":[13],"to":[14,31,42,59,67,86,133],"the":[15,32,76,88,123],"subtle":[16],"differences":[17],"between":[18],"gaits":[19],"and":[20,63,95,141,154,158,187],"numerous":[21],"confounding":[22],"factors.":[23],"Recent":[24],"advancements":[25],"self-supervised":[27,83],"pretraining":[28],"have":[29,49],"led":[30],"development":[33,52],"of":[34,90,106,116],"robust":[35],"gait":[36,68,84,99,192],"models":[38],"that":[39,156],"are":[40],"invariant":[41],"walking":[43,119],"covariates.":[44],"While":[45],"neural":[46],"scaling":[47,80,135,160],"laws":[48,136],"transformed":[50],"model":[51,61,93,139],"other":[54],"domains":[55],"by":[56,170],"linking":[57],"performance":[58,128,150,188],"data,":[60,138],"size,":[62,140],"compute,":[64],"their":[65],"applicability":[66],"remains":[69],"unexplored.":[70],"In":[71],"this":[72],"work,":[73],"we":[74],"conduct":[75],"first":[77],"empirical":[78],"study":[79],"on":[81,97,113],"skeleton-based":[82],"quantify":[87],"effect":[89],"data":[91,157],"quantity,":[92],"size":[94],"compute":[96,159,177],"downstream":[98,163],"performance.":[101],"We":[102,125,165],"pretrain":[103],"multiple":[104],"variants":[105],"GaitPT":[107,172],"--":[108,112],"transformer-based":[110],"architecture":[111],"dataset":[115],"2.7":[117],"million":[118],"sequences":[120],"collected":[121],"wild.":[124],"evaluate":[126],"zero-shot":[127],"across":[129],"four":[130],"benchmark":[131],"datasets":[132],"derive":[134],"for":[137,190],"compute.":[142],"Our":[143],"findings":[144],"demonstrate":[145],"predictable":[146],"power-law":[147],"improvements":[148],"with":[151,173],"increased":[152],"scale":[153],"confirm":[155],"significantly":[161],"influence":[162],"accuracy.":[164],"further":[166],"isolate":[167],"architectural":[168],"contributions":[169],"comparing":[171],"GaitFormer":[174],"under":[175],"controlled":[176],"budgets.":[178],"These":[179],"results":[180],"provide":[181],"practical":[182],"insights":[183],"into":[184],"resource":[185],"allocation":[186],"estimation":[189],"real-world":[191],"systems.":[194]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-18T00:00:00"}